{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "-YibpL3v8Sf8",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "from collections import defaultdict\n",
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "import sys\n",
        "import os\n",
        "os.environ['KERAS_BACKEND']='theano' # Why theano why not\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.layers import Embedding\n",
        "from keras.layers import Dense, Input, Flatten\n",
        "from keras.layers import Conv1D, MaxPooling1D, Embedding, Dropout\n",
        "from keras.models import Model\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "import matplotlib.pyplot as plt\n",
        "plt.switch_backend('agg')\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n8z5XNCd99Vt",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def clean_str(string):\n",
        "    string = re.sub(r\"\\\\\", \"\", string)\n",
        "    string = re.sub(r\"\\'\", \"\", string)\n",
        "    string = re.sub(r\"\\\"\", \"\", string)\n",
        "    return string.strip().lower()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LG-0Ij2V-Bjl",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "MAX_SEQUENCE_LENGTH = 1000\n",
        "MAX_NB_WORDS = 20000\n",
        "EMBEDDING_DIM = 100\n",
        "VALIDATION_SPLIT = 0.2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xU-Gzp5X-G0_",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d4a47aee-ee55-4685-9e09-93f603ab68b8",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530788057022,
          "user_tz": -330,
          "elapsed": 1219,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# reading data\n",
        "df = pd.read_excel('dataset.xlsx')\n",
        "df = df.dropna()\n",
        "df = df.reset_index(drop=True)\n",
        "print('Shape of dataset ',df.shape)\n",
        "print(df.columns)\n",
        "print('No. of unique classes',len(set(df['class'])))"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of dataset  (100, 2)\n",
            "Index(['message', 'class'], dtype='object')\n",
            "No. of unique classes 17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yTg_bT92-Lmu",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "macronum=sorted(set(df['class']))\n",
        "macro_to_id = dict((note, number) for number, note in enumerate(macronum))\n",
        "\n",
        "def fun(i):\n",
        "    return macro_to_id[i]\n",
        "\n",
        "df['class']=df['class'].apply(fun)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1iuuRu3z-dUl",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "556dd53b-d66d-4a17-f0d6-2c50de94c13f",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530788064257,
          "user_tz": -330,
          "elapsed": 1064,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "texts = []\n",
        "labels = []\n",
        "\n",
        "\n",
        "for idx in range(df.message.shape[0]):\n",
        "    text = BeautifulSoup(df.message[idx])\n",
        "    texts.append(clean_str(str(text.get_text().encode())))\n",
        "\n",
        "for idx in df['class']:\n",
        "    labels.append(idx)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html5lib\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
            "\n",
            "The code that caused this warning is on line 193 of the file /usr/lib/python3.6/runpy.py. To get rid of this warning, change code that looks like this:\n",
            "\n",
            " BeautifulSoup(YOUR_MARKUP})\n",
            "\n",
            "to this:\n",
            "\n",
            " BeautifulSoup(YOUR_MARKUP, \"html5lib\")\n",
            "\n",
            "  markup_type=markup_type))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "UybzQTeT-jGj",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fee861a5-5673-4b6e-ccd7-f08ccdebf76c",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530788068659,
          "user_tz": -330,
          "elapsed": 1410,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words=MAX_NB_WORDS)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "sequences = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "print('Number of Unique Tokens',len(word_index))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Unique Tokens 932\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lammuHZ3-puo",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3ac2e35b-2ab4-48ab-aab8-2253748e9270",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530788073777,
          "user_tz": -330,
          "elapsed": 1190,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "\n",
        "labels = to_categorical(np.asarray(labels))\n",
        "print('Shape of Data Tensor:', data.shape)\n",
        "print('Shape of Label Tensor:', labels.shape)\n",
        "\n",
        "indices = np.arange(data.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "data = data[indices]\n",
        "labels = labels[indices]\n",
        "nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])\n",
        "\n",
        "x_train = data[:-nb_validation_samples]\n",
        "y_train = labels[:-nb_validation_samples]\n",
        "x_val = data[-nb_validation_samples:]\n",
        "y_val = labels[-nb_validation_samples:]"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of Data Tensor: (100, 1000)\n",
            "Shape of Label Tensor: (100, 17)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1cia1lKp8YN2",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ddd4e1b5-a416-484d-8580-86816aeff61d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530788092979,
          "user_tz": -330,
          "elapsed": 12325,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "embeddings_index = {}\n",
        "f = open('glove.6B.100d.txt',encoding='utf8')\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    embeddings_index[word] = coefs\n",
        "f.close()\n",
        "\n",
        "print('Total %s word vectors in Glove 6B 100d.' % len(embeddings_index))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total 400000 word vectors in Glove 6B 100d.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mvbOk28C-ywX",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        # words not found in embedding index will be all-zeros.\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "\n",
        "embedding_layer = Embedding(len(word_index) + 1,\n",
        "                            EMBEDDING_DIM,weights=[embedding_matrix],\n",
        "                            input_length=MAX_SEQUENCE_LENGTH,trainable=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WWAEbYKN-5dL",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "c4bebdf8-16e0-4982-b524-c90ee3f05173",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530788208818,
          "user_tz": -330,
          "elapsed": 1926,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
        "embedded_sequences = embedding_layer(sequence_input)\n",
        "l_cov1= Conv1D(128, 5, activation='relu')(embedded_sequences)\n",
        "l_pool1 = MaxPooling1D(5)(l_cov1)\n",
        "l_cov2 = Conv1D(128, 5, activation='relu')(l_pool1)\n",
        "l_pool2 = MaxPooling1D(5)(l_cov2)\n",
        "l_cov3 = Conv1D(128, 5, activation='relu')(l_pool2)\n",
        "l_pool3 = MaxPooling1D(35)(l_cov3)  # global max pooling\n",
        "l_flat = Flatten()(l_pool3)\n",
        "l_dense = Dense(128, activation='relu')(l_flat)\n",
        "preds = Dense(len(macronum), activation='softmax')(l_dense)\n",
        "\n",
        "model = Model(sequence_input, preds)\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['acc'])\n",
        "\n",
        "print(\"Simplified convolutional neural network\")\n",
        "model.summary()\n",
        "cp=ModelCheckpoint('model_cnn.hdf5',monitor='val_acc',verbose=1,save_best_only=True)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Simplified convolutional neural network\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         (None, 1000)              0         \n",
            "_________________________________________________________________\n",
            "embedding_1 (Embedding)      (None, 1000, 100)         93300     \n",
            "_________________________________________________________________\n",
            "conv1d_10 (Conv1D)           (None, 996, 128)          64128     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_10 (MaxPooling (None, 199, 128)          0         \n",
            "_________________________________________________________________\n",
            "conv1d_11 (Conv1D)           (None, 195, 128)          82048     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_11 (MaxPooling (None, 39, 128)           0         \n",
            "_________________________________________________________________\n",
            "conv1d_12 (Conv1D)           (None, 35, 128)           82048     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_12 (MaxPooling (None, 1, 128)            0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 17)                2193      \n",
            "=================================================================\n",
            "Total params: 340,229\n",
            "Trainable params: 340,229\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GET42RUmCG1s",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 1074
        },
        "outputId": "c85f9471-8081-404f-abfc-528e9dfaceab",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530787445619,
          "user_tz": -330,
          "elapsed": 63069,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "history=model.fit(x_train, y_train, validation_data=(x_val, y_val),epochs=15, batch_size=2,callbacks=[cp])"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 80 samples, validate on 20 samples\n",
            "Epoch 1/15\n",
            "80/80 [==============================] - 4s 49ms/step - loss: 2.6623 - acc: 0.2500 - val_loss: 2.3584 - val_acc: 0.2500\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.25000, saving model to model_cnn.hdf5\n",
            "Epoch 2/15\n",
            "80/80 [==============================] - 4s 50ms/step - loss: 1.9873 - acc: 0.4375 - val_loss: 1.8733 - val_acc: 0.4500\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.25000 to 0.45000, saving model to model_cnn.hdf5\n",
            "Epoch 3/15\n",
            "80/80 [==============================] - 4s 50ms/step - loss: 1.2715 - acc: 0.6625 - val_loss: 1.4019 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00003: val_acc improved from 0.45000 to 0.65000, saving model to model_cnn.hdf5\n",
            "Epoch 4/15\n",
            "80/80 [==============================] - 4s 49ms/step - loss: 0.9800 - acc: 0.7250 - val_loss: 1.5273 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00004: val_acc did not improve from 0.65000\n",
            "Epoch 5/15\n",
            "54/80 [===================>..........] - ETA: 1s - loss: 0.7271 - acc: 0.8333"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 4s 50ms/step - loss: 0.7497 - acc: 0.8250 - val_loss: 1.1414 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00005: val_acc did not improve from 0.65000\n",
            "Epoch 6/15\n",
            "80/80 [==============================] - 4s 49ms/step - loss: 0.6599 - acc: 0.8500 - val_loss: 1.2248 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00006: val_acc did not improve from 0.65000\n",
            "Epoch 7/15\n",
            "80/80 [==============================] - 4s 50ms/step - loss: 0.5452 - acc: 0.8875 - val_loss: 1.1178 - val_acc: 0.7000\n",
            "\n",
            "Epoch 00007: val_acc improved from 0.65000 to 0.70000, saving model to model_cnn.hdf5\n",
            "Epoch 8/15\n",
            "80/80 [==============================] - 4s 49ms/step - loss: 0.4755 - acc: 0.8625 - val_loss: 1.0355 - val_acc: 0.7500\n",
            "\n",
            "Epoch 00008: val_acc improved from 0.70000 to 0.75000, saving model to model_cnn.hdf5\n",
            "Epoch 9/15\n",
            "80/80 [==============================] - 4s 49ms/step - loss: 0.4063 - acc: 0.8750 - val_loss: 1.3590 - val_acc: 0.7000\n",
            "\n",
            "Epoch 00009: val_acc did not improve from 0.75000\n",
            "Epoch 10/15\n",
            "16/80 [=====>........................] - ETA: 2s - loss: 1.1223 - acc: 0.8750"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 4s 49ms/step - loss: 0.4028 - acc: 0.9250 - val_loss: 1.2219 - val_acc: 0.8000\n",
            "\n",
            "Epoch 00010: val_acc improved from 0.75000 to 0.80000, saving model to model_cnn.hdf5\n",
            "Epoch 11/15\n",
            "80/80 [==============================] - 4s 49ms/step - loss: 0.4737 - acc: 0.8875 - val_loss: 1.1734 - val_acc: 0.8000\n",
            "\n",
            "Epoch 00011: val_acc did not improve from 0.80000\n",
            "Epoch 12/15\n",
            "80/80 [==============================] - 4s 49ms/step - loss: 0.2344 - acc: 0.9250 - val_loss: 1.3924 - val_acc: 0.8000\n",
            "\n",
            "Epoch 00012: val_acc did not improve from 0.80000\n",
            "Epoch 13/15\n",
            "80/80 [==============================] - 4s 50ms/step - loss: 0.1823 - acc: 0.9750 - val_loss: 1.7238 - val_acc: 0.8000\n",
            "\n",
            "Epoch 00013: val_acc did not improve from 0.80000\n",
            "Epoch 14/15\n",
            "80/80 [==============================] - 4s 49ms/step - loss: 0.4431 - acc: 0.9375 - val_loss: 1.4792 - val_acc: 0.8000\n",
            "\n",
            "Epoch 00014: val_acc did not improve from 0.80000\n",
            "Epoch 15/15\n",
            "10/80 [==>...........................] - ETA: 3s - loss: 0.1110 - acc: 0.9000"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 4s 49ms/step - loss: 0.1814 - acc: 0.9250 - val_loss: 1.5112 - val_acc: 0.8000\n",
            "\n",
            "Epoch 00015: val_acc did not improve from 0.80000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Hw6nPvUG_js0",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "f9404a8a-f642-427f-c252-172d2f1d8036",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530787456262,
          "user_tz": -330,
          "elapsed": 1410,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "fig1 = plt.figure()\n",
        "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves :CNN',fontsize=16)\n",
        "fig1.savefig('loss_cnn.png')\n",
        "plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vzp/X0by5n6uBO+kFP+mF/9o1DTZb5qsF7HayfH1Ij8aRieZyIURj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\nnt9+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa600e750f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "akd1Jx2a_kbh",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "8807113d-cb58-463b-b2cf-71a8cb6abe99",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530787460989,
          "user_tz": -330,
          "elapsed": 1312,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "fig2=plt.figure()\n",
        "plt.plot(history.history['acc'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_acc'],'b',linewidth=3.0)\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves : CNN',fontsize=16)\n",
        "fig2.savefig('accuracy_cnn.png')\n",
        "plt.show()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bvYedFgvxH3yM44raPo5KFNaMGYF8/XXmzFSTJ6fQsKHDhxGJssbrbQpa62dz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\nRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa600eac278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "BH5VmVtmf9_R",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "#from keras.utils.vis_utils import plot_model\n",
        "#plot_model(model, to_file='cnn_model.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UBo-Zv-WkcHJ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 1197
        },
        "outputId": "9fedbf0c-257a-413c-c4fe-f03e8780d906",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530797250743,
          "user_tz": -330,
          "elapsed": 1478,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "display(Image.open('cnn_model.png'))"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MxIULF2BiUnnVZe3atRg8eDA2bdqE//f//h/GjRuHvn37ws/PD3l5eSgrK8PkyZPRrl07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\nZWUlQkJCVLb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D//qv/xIu88fExODZs2fYsWOHsBRtdHQ0iAgZGRmQyWQ4fPgw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\nGDNx/x//aMwqJw8yFwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=507x1180 at 0x7FA5FF22D198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Sdom4oW-mW1V",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
